Natural question generation via reinforcement learning based graph-to-sequence model

ABSTRACT

For a passage text and a corresponding answer text, perform a word-level soft alignment to obtain contextualized passage embeddings and contextualized answer embeddings, and a hidden level soft alignment on the contextualized passage embeddings and the contextualized answer embeddings to obtain a passage embedding matrix. Construct a passage graph of the passage text based on the passage embedding matrix, and apply a bidirectional gated graph neural network to the passage graph until a final state embedding is determined, during which intermediate node embeddings are fused from both incoming and outgoing edges. Obtain a graph-level embedding from the final state embedding, and decode the final state embedding to generate an output sequence word-by-word. Train a machine learning model to generate at least one question corresponding to the passage text and the answer text, by evaluating the output sequence with a hybrid evaluator combining cross-entropy evaluation and reinforcement learning evaluation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application62/956,488 filed Jan. 2, 2020, the complete disclosure of which isexpressly incorporated by reference herein, in its entirety, for allpurposes.

STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINTINVENTOR

The following disclosure(s) are submitted under 35 U.S.C. 102(b)(1)(A):

-   Yu Chen, Lingfei Wu, Mohammed J. Zaki, Reinforcement Learning Based    Graph-to-Sequence Model for Natural Question Generation, arXiv:    1908.04942, Version 1, 14 Aug. 2019.-   Yu Chen, Lingfei Wu, Mohammed J. Zaki, Reinforcement Learning Based    Graph-to-Sequence Model for Natural Question Generation, arXiv:    1908.04942, Version 2, 20 Dec. 2019.-   Yu Chen, Lingfei Wu, Mohammed J. Zaki, Reinforcement Learning Based    Graph-to-Sequence Model for Natural Question Generation, arXiv:    1908.04942, Version 3, 16 Feb. 2020.

BACKGROUND

The present invention relates to the electrical, electronic and computerarts, and more specifically, to machine learning systems and the like.

Natural question generation (QG) is a challenging yet rewarding task,that aims to generate questions given an input passage and a targetanswer. Applications include, for example, reading comprehension,question answering, dialog systems, information technology (IT) support,and the like.

Known solutions typically do not consider global interactions betweenanswer and context; fail to consider the rich hidden structuralinformation of the word sequence; and/or are subject to limitations ofcross-entropy based objectives.

SUMMARY

Principles of the invention provide techniques for natural questiongeneration via reinforcement learning based graph-to-sequence model. Inone aspect, an exemplary method includes the step of for a passage textand a corresponding answer text, performing a word-level soft alignmentto obtain contextualized passage embeddings and contextualized answerembeddings; performing a hidden level soft alignment on thecontextualized passage embeddings and the contextualized answerembeddings to obtain a passage embedding matrix; constructing a passagegraph of the passage text based on the passage embedding matrix; andapplying a bidirectional gated graph neural network to the passage graphuntil a final state embedding is determined, during which applicationintermediate node embeddings are fused from both incoming and outgoingedges of the graph. Further steps include obtaining a graph-levelembedding from the final state embedding; decoding the final stateembedding to generate an output sequence word-by-word; and training amachine learning model to generate at least one question correspondingto the passage text and the answer text, by evaluating the outputsequence with a hybrid evaluator combining cross-entropy evaluation andreinforcement learning evaluation.

In another aspect, an exemplary apparatus includes a memory; anon-transitory computer readable medium including computer executableinstructions; and at least one processor, coupled to the memory and thenon-transitory computer readable medium, and operative to execute theinstructions to be operative to instantiate a deep alignment network, agraph encoder including a bidirectional gated graph neural network, adecoder, and a hybrid evaluator; with the deep alignment network, for apassage text and a corresponding answer text, perform a word-level softalignment to obtain contextualized passage embeddings and contextualizedanswer embeddings; and, with the deep alignment network, perform ahidden level soft alignment on the contextualized passage embeddings andthe contextualized answer embeddings to obtain a passage embeddingmatrix. The at least one processor is further operative to, with thegraph encoder, construct a passage graph of the passage text based onthe passage embedding matrix; apply the bidirectional gated graph neuralnetwork to the passage graph until a final state embedding isdetermined, during which application intermediate node embeddings arefused from both incoming and outgoing edges of the graph; with the graphencoder, obtain a graph-level embedding from the final state embedding;with the decoder, decode the final state embedding to generate an outputsequence word-by-word; and train a machine learning model to generate atleast one question corresponding to the passage text and the answertext, by evaluating the output sequence with the hybrid evaluatorcombining cross-entropy evaluation and reinforcement learningevaluation.

As used herein, “facilitating” an action includes performing the action,making the action easier, helping to carry the action out, or causingthe action to be performed. Thus, by way of example and not limitation,instructions executing on one processor might facilitate an actioncarried out by instructions executing on a remote processor, by sendingappropriate data or commands to cause or aid the action to be performed.For the avoidance of doubt, where an actor facilitates an action byother than performing the action, the action is nevertheless performedby some entity or combination of entities.

One or more embodiments of the invention or elements thereof can beimplemented in the form of a computer program product including acomputer readable storage medium with computer usable program code forperforming the method steps indicated. Furthermore, one or moreembodiments of the invention or elements thereof can be implemented inthe form of a system (or apparatus) including a memory, and at least oneprocessor that is coupled to the memory and operative to performexemplary method steps. Yet further, in another aspect, one or moreembodiments of the invention or elements thereof can be implemented inthe form of means for carrying out one or more of the method stepsdescribed herein; the means can include (i) hardware module(s), (ii)software module(s) stored in a computer readable storage medium (ormultiple such media) and implemented on a hardware processor, or (iii) acombination of (i) and (ii); any of (i)-(iii) implement the specifictechniques set forth herein.

Techniques of the present invention can provide substantial beneficialtechnical effects. For example, one or more embodiments provide areinforcement learning (RL) based graph-to-sequence (Graph2Seq) modelfor QG as well as deep alignment networks to effectively cope with theQG task, which overcomes limitations of existing approaches, such as (i)ignoring the rich structure information hidden in text, (ii) solelyrelying on cross-entropy loss that leads to issues like exposure biasand inconsistency between train/test measurement, and (iii) failing tofully exploit the answer information.

These and other features and advantages of the present invention willbecome apparent from the following detailed description of illustrativeembodiments thereof, which is to be read in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 3 is a block diagram of an exemplary system, according to an aspectof the invention;

FIG. 4 shows an attention-based soft-alignment mechanism, according toan aspect of the invention;

FIG. 5 is a table of automatic evaluation results on the SQuAD test set,according to an aspect of the invention;

FIG. 6 is a table of human evaluation results (+/−standard deviation) onthe SQuAD split-2 test set, wherein the rating scale is from 1 to 5(higher scores indicate better results), according to an aspect of theinvention;

FIG. 7 is a table showing results for an ablation study on the SQuADsplit-2 test set, according to an aspect of the invention;

FIG. 8 is a table showing generated questions on SQuAD split-2 test set,with target answers underlined, according to an aspect of the invention;

FIG. 9 is a graph showing the effect of the number of GNN hops,according to an aspect of the invention;

FIG. 10 is a table showing additional results for an ablation study onthe SQuAD split-2 test set, according to an aspect of the invention; and

FIG. 11 depicts a computer system that may be useful in implementing oneor more aspects and/or elements of the invention, also representative ofa cloud computing node according to an embodiment of the presentinvention.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a cloud-based service 96 for naturalquestion generation via reinforcement learning based graph-to-sequencemodel, it being understood that cloud, non-cloud, and combinedapproaches could be employed.

One or more embodiments provide a method and system for natural questiongeneration via a reinforcement learning based graph-to-sequence model.Question generation is an important task in the field of naturallanguage processing. Natural question generation (QG) is a challengingyet rewarding task, which aims to generate questions given an inputpassage and a target answer. Exemplary applications include readingcomprehension, question answering, dialog systems, informationtechnology (IT) support, and the like.

Heretofore, current systems have failed to consider global interactionsbetween answer and context; one or more embodiments, in contrast,provide a deep alignment network to align answer and context.Furthermore, current systems have generally failed to consider richhidden structural information of word sequences; one or moreembodiments, in contrast, provide a novel Graph2Seq model forconsidering hidden structural information in sequence. Even further,current systems have limitations as to cross-entropy based objectives;one or more embodiments, in contrast, provide a novel ReinforcementLearning Loss for enforcing syntactic and semantic coherence ofgenerated text.

Advantageously, one or more embodiments do not rely on “hand-crafted”rules or features. One or more embodiments provide an effective DeepAlignment Network for explicitly modeling answer information. One ormore embodiments apply Graph Neural Networks (GNNs) to extend Seq2Seqarchitecture to Graph2Seq architecture.

One or more embodiments provide a novel reinforcement learning basedGraph-to-Sequence model for natural question generation. Aspects includea novel Reinforcement Learning (RL) based Graph2Seq model for naturalquestion generation, as well as a two-stage training strategy to trainthe model with both cross-entropy and RL losses. A number of differentways of constructing passage graphs are disclosed herein and theirperformance impact on a Graph Neural Network (GNN)-based encoder isconsidered. One or more embodiments provide a simple yet effective DeepAlignment Network for explicitly modeling answer information.

Reference should now be had to FIG. 3, which depicts a system 300 inaccordance with an aspect of the invention. Inputs to deep alignmentnetwork 303 include a passage 305 and answer 307. Passage 305 caninclude, for example, a paragraph of a document, or other sequence oftext. The answer 307 can include, for example, a few words or a phrase;in general, a short text sequence. The goal of one or more embodimentsis to generate a question that relates to the passage and the answer.For example, in the field of reading comprehension, a subject will reada passage and then be given a series of questions about it. Based on thequestions, the subject can examine the passage to determine theanswer(s). Here, in contrast, a passage and answers are available, andit is desired to generate questions that correspond to the answers. Deepalignment network 303 deeply explores the relationship between thepassage and the answer, so that the neural network (e.g. graph neuralnetwork (GNN) 309) can capture the deep interactions between them. Then,pass the node embeddings 311 that worked for each passage to graphencoder 313.

Graph encoder 313 takes, as input, a suitable graph 315. Each h at 317includes a node embedding 311 of graph 315. In GNN 309, to carry outembeddings, aggregate the neighborhood node embeddings information tolearn the context so that the embeddings can be learned. When learningembeddings, consider both incoming edges and outgoing edges. Forexample, for node 319, the incoming edges are 321, 323, and the outgoingedges are 325, 327. Incoming edges and outgoing edges have differenttypes of information, which should be fused, as at 317. Bar 329represents the outgoing edges and bar 331 represents the incoming edges.For each node 319, fuse the incoming and outgoing edges 329, 331 toobtain a corresponding one of the node embeddings 311 for that node.Once the node embeddings 311 are available, there are, for example, twoways to construct graph 315. One way is to use a static graph based on atree structure including word sequences. The tree is then an input tothe graph encoder 313. Another way is Semantics-aware dynamic graphconstruction. Each graph node will have a node embedding 311. Graph(label) embedding 333, in essence, summarizes the whole graph; it can beobtained from the node embeddings 311 using linear projection andMaxpool, as at 337.

The output of encoder 313 is then taken as the input to LSTM decoder335. Decoder 335 decodes the input into a text sequence 339 (the desiredquestion corresponding to the passage and answer 305, 307). Y^(sample)represents one instance; each decoded sequence is compared to the“golden” sequence Y^(gold). Comparison can be carried out, for example,with hybrid evaluator 341, which includes cross-entropy evaluator 343and RL-based evaluator 345. The output of evaluator 341 is a reward 347to compute the final loss. This is provided back to the deep alignmentnetwork 303 and training continues until convergence. Once training iscomplete, the trained model is used to generate high-quality questions,given passages and answers.

Deep alignment network 303 can be implemented, for example, byword-level answer alignment and hidden representation-level answeralignment. In word-level answer alignment, first carry outsoft-alignment at the word-level, based on the GloVe (Global Vectors forWord Representation) embeddings of the passage and the answer, to obtainaligned answer embeddings. Encode each passage word with GloVeembedding, BERT (Bidirectional Encoder Representations fromTransformers) embedding, aligned answer embedding and linguisticfeature(s) (e.g., case, Part-of-Speech (POS) and Named EntityRecognition (NER)). Encode each answer word with GloVe embedding andBERT embedding. Apply two BiLSTMs (Bidirectional long short-termmemories) to the encoded passage and answer, respectively. In hiddenrepresentation-level answer alignment, carry out soft-alignment at thecontextualized hidden representation level based on the contextualizedpassage and answer embeddings to obtain aligned answer embeddings. Applya BiLSTM to the concatenation of the contextualized passage embeddingsand the above aligned answer embeddings.

Graph encoder 313 can be implemented, for example, via graphconstruction and using bidirectional gated graph neural networks. Ingraph construction, construct a passage graph which includes eachpassage word as a node. Different ways of constructing a passage graphinclude syntax-based static graph construction and semantic-awaredynamic graph construction. One or more embodiments use a bidirectionalgated graph neural network (BiGGNN) to encode the directed passagegraph. Compute the graph-level representation, for example, by applyinglinear projection and max pooling to the updated node embeddings.

Decoder 335 includes, for example, a state-of-the-art RNN-based decoder,with an attention-based LSTM model, copy mechanism, and coveragemechanism. This results in an improved loss function.

Hybrid evaluator 341 combines, for example, both cross-entropy evaluator343 and RL-based evaluator 345. In the first stage, train the modelusing regular cross-entropy loss. In the second stage, fine-tune themodel by optimizing a mixed objective function combing bothcross-entropy loss and reinforcement loss which is defined on theevaluation metric (e.g., BLEU (Bilingual Evaluation Understudy) score).

One or more embodiments accordingly provide a method and system forperforming natural question generation by learning a novel RL basedGraph2Seq model, including a Deep Alignment Network 303 that carries outword-level answer alignment and hidden representation-level answeralignment; a Graph Encoder 313 that constructs a passage graph, appliesbidirectional gated graph neural networks to the passage graph, andcomputes the graph-level embedding; a Decoder 335 that applies anattention-based LSTM decoder with copy and coverage mechanisms; and aHybrid evaluator 341 that combines both a cross-entropy evaluator and anRL-based evaluator to train the model.

One non-limiting practical application is a dialog system for ITsupport. For example, consider a virtual assistant for answering userquestions. Question and answer pairs are not always available. One mightextract the key points from the user manual for a laptop computer; thesekey points are identified as answers. One example might be the procedurefor installing a piece of application software. Using a system inaccordance with an aspect of the invention, generate the questions thatcorrespond to that answer. Then, when an actual user asks that question,the system provides the relevant passage in answer thereto. Generally,embodiments of the invention can be applied, for example, to a “how to”manual to generate likely questions anticipated from users, and toidentify the corresponding portions of the manual that constitute theanswers. For example, provide a cloud service for network users,identify questions in network documentation using embodiments of theinvention, and respond to actual users asking those questions. Thenetwork could be fixed/reconfigured in accordance with the identifiedanswer(s), for example.

As noted, natural question generation (QG) aims to generate questionsfrom a passage and an answer. Previous works on QG: (i) ignore the richstructure information hidden in text, (ii) solely rely on cross-entropyloss that leads to issues such as exposure bias and inconsistencybetween train/test measurement, and/or (iii) fail to fully exploit theanswer information. To address these limitations, one or moreembodiments provide a reinforcement learning (RL) basedgraph-to-sequence (Graph2Seq) model for QG. One or more embodimentsinclude a Graph2Seq generator with a novel Bidirectional Gated GraphNeural Network-based encoder to embed the passage, and a hybridevaluator with a mixed objective function that combines both thecross-entropy and RL loss to ensure the generation of syntactically andsemantically valid text. One or more embodiments provide an effectiveDeep Alignment Network for incorporating the answer information into thepassage at both the word and contextual level. One or more embodimentsare end-to-end trainable and outperform existing methods by asignificant margin on the standard SQuAD benchmark for QG.

Natural question generation (QG) has many useful applications such asimproving the question answering task by providing more training data,generating practice exercises and assessments for educational purposes,and helping dialog systems to kick-start and continue a conversationwith human users. While many existing works focus on QG from images orknowledge bases, one or more embodiments are advantageously capable ofQG from text.

Conventional methods for QG rely on heuristic rules or hand-craftedtemplates, leading to the issues of low generalizability andscalability. Recent attempts have been focused on exploiting NeuralNetwork (NN) based approaches that do not require manually-designedrules and are end-to-end trainable. Encouraged by the huge success ofneural machine translation, these approaches formulate the QG task as asequence-to-sequence (Seq2Seq) learning problem. Specifically,attention-based Seq2Seq models and their enhanced versions with copy andcoverage mechanisms have been widely applied and show promising resultson this task. However, these methods typically ignore the hiddenstructural information associated with a word sequence such as thesyntactic parsing tree. Failing to utilize the rich text structureinformation beyond the simple word sequence may limit the effectivenessof these models for QG.

It has been observed that in general, cross-entropy based sequencetraining has several limitations such as exposure bias and inconsistencybetween train/test measurement. As a result, such training does notalways produce the best results on discrete evaluation metrics onsequence generation tasks such as text summarization or questiongeneration. To cope with these issues, some recent QG approachesdirectly optimize evaluation metrics using Reinforcement Learning (RL).However, existing approaches usually only employ evaluation metrics suchas BLEU and ROUGE-L as rewards for RL optimization. More importantly,they do not exploit other important metrics such as syntactic andsemantic constraints for guiding high-quality text generation.

Early works on neural QG did not take into account the answerinformation when generating a question. Recent works have started toexplore various means of utilizing the answer information. When questiongeneration is guided by the semantics of an answer, the resultingquestions become more relevant and readable. Conceptually, there arethree different ways to incorporate the answer information by simplymarking the answer location in the passage, using complex passage-answermatching strategies, or separating answers from passages when applying aSeq2Seq model. However, they neglect potential semantic relationsbetween passage words and answer words, and thus do not explicitly modelthe global interactions among them in the embedding space.

To address these aforementioned issues, one or more embodiments providea novel reinforcement learning based generator-evaluator architecturethat: i) makes full use of rich hidden structure information beyond thesimple word sequence; ii) generates syntactically and semantically validtext while maintaining the consistency of train/test measurement; and/oriii) models explicitly the global interactions of semantic relationshipsbetween passage and answer at both word-level and contextual-level. Inparticular, to achieve the first goal, one or more embodiments constructa syntax-based static graph and/or a semantics-aware dynamic graph fromthe text sequence, as well as its rich hidden structure information. Oneor more embodiments provide a graph-to-sequence (Graph2Seq) model basedgenerator that encodes the graph representation of a text passage anddecodes a question sequence using a Recurrent Neural Network (RNN). Aninventive Graph2Seq model is based on a novel bidirectional gated graphneural network, which extends the original gated graph neural network byconsidering both incoming and outgoing edges, and fusing them during thegraph embedding learning. To achieve the second goal, one or moreembodiments provide a hybrid evaluator which is trained by optimizing amixed objective function that combines both cross-entropy and RL loss.One or more embodiments use not only discrete evaluation metrics such asBLEU, but also semantic metrics such as word mover's distance toencourage both syntactically and semantically valid text generation. Toachieve the third goal, one or more embodiments employ a novel DeepAlignment Network (DAN) for effectively incorporating answer informationinto the passage at multiple granularity levels.

One or more embodiments provide a novel RL-based Graph2Seq model fornatural question generation, introducing the Graph2Seq architecture forQG. One or more embodiments provide static and/or dynamic ways ofconstructing a graph from text and enabling their effective performanceimpacts on a GNN encoder. In one or more embodiments, an inventive modelis end-to-end trainable, achieves new state-of-the-art scores, andoutperforms existing methods by a significant margin on the standardSQuAD benchmark for QG. Experiments demonstrate that the questionsgenerated by one or more embodiments are more natural (semantically andsyntactically) compared to other baselines.

RL-based generator-evaluator architecture: The question generation taskis now defined, and an exemplary inventive RL-based Graph2Seq model forquestion generation is disclosed. The goal of question generation is togenerate natural language questions based on a given form of data, suchas knowledge base triples or tables, sentences, or images, where thegenerated questions need to be answerable from the input data. One ormore embodiments focus on QG from a given text passage, along with atarget answer.

Assume that a text passage is a collection of word tokens X^(p)={x₁^(p), x₂ ^(p), . . . , x_(N) ^(p)}, and a target answer is also acollection of word tokens X^(a)={x₁ ^(a), x₂ ^(a), . . . , x_(L) ^(a)}.The task of natural question generation is to generate the best naturallanguage question consisting of a sequence of word tokens {circumflexover (X)}={y₁, y₂, . . . , y_(T)} which maximizes the conditionallikelihood Ŷ=arg max_(Y) P(Y|X^(p), X^(a)). Here N, L, and T are thelengths of the passage, answer, and question, respectively. One or moreembodiments address the problem setting where there are a set of passage(and answers) and target questions pairs, to learn the mapping; existingQG approaches make a similar assumption. In one or more embodiments,when training the model, feed in a plurality of passage-question pairs,as well as the associated answers; the system learns the mappings fromthe passages to the questions, given the data.

Deep Alignment Network: Regarding network 303, answer information ispertinent for generating relevant and high quality questions from apassage. Unlike previous methods that neglect potential semanticrelations between passage and answer words, one or more embodimentsexplicitly model the global interactions among them in the embeddingspace. To this end, one or more instances provide a novel Deep AlignmentNetwork (DAN) component 303 for effectively incorporating answerinformation into the passage with multiple granularity levels.Specifically, one or more embodiments perform attention-basedsoft-alignment at the word level, as well as at the contextualizedhidden state level, so that multiple levels of alignments can help learnhierarchical representations.

Referring to FIG. 4, Let X^(p)∈

^(F×N) and X^(p)∈

^({tilde over (F)}) ^(p) ^(×N) denote two embeddings associated withpassage text. Similarly, let X^(a)∈

^(F×L) and {tilde over (X)}^(a)∈

^({tilde over (F)}) ^(a) ^(×L) denote two embeddings associated withanswer text. Conceptually, as shown in FIG. 4, the soft alignmentmechanism includes three steps: i) compute the attention score β_(i,j)for each pair of passage word x_(i) ^(p) and answer word x_(j) ^(a); ii)multiply, as at 401, the attention matrix β with the answer embeddings{tilde over (X)}^(a) to obtain the aligned answer embeddings H^(p) 403for the passage; and iii) concatenate, as at 405, the resulting alignedanswer embeddings W with the passage embeddings {tilde over (X)}^(p) toobtain the final passage embeddings 407, {tilde over (H)}^(p)∈

^(({tilde over (F)}) ^(p) ^(+{tilde over (F)}) ^(a) ^()×N).

The soft-alignment function is defined as follows:

{tilde over (H)} ^(p)=Align(X ^(p) ,X ^(a) ,{tilde over (X)} ^(p),{tilde over (X)} ^(a))=CAT({tilde over (X)} ^(p) ;H ^(p))=CAT({tildeover (X)} ^(p) ;{tilde over (X)} ^(a)β^(T))  (1)

In the above, the matrix {tilde over (H)}^(p) is the final passageembedding, the function CAT is a simple concatenation operation, and βis an N×L attention score matrix, computed by:

β∝exp(ReLU(WX ^(p))^(T)ReLU(WX ^(a)))  (2)

In the above, W∈

^(d×F) is a trainable weight matrix, with d being the hidden state sizeand ReLU is the rectified linear unit. After introducing the generalsoft-alignment mechanism, next consider how to undertake soft-alignmentat both the word-level and the contextualized hidden state level.

Word-Level Alignment: In the word-level alignment stage, first perform asoft-alignment between the passage and the answer based only on theirpretrained GloVe embeddings and compute the final passage embeddings by{tilde over (H)}^(p)=Align(G^(p), G^(a), [G^(p); B^(p); L^(p)], G^(a)),where G^(p), B^(p), and L^(p) are the corresponding GloVe embedding,BERT embedding, and linguistic feature (i.e., case, NER and POS)embedding of the passage text, respectively. Then a bidirectional LSTMis applied to the final passage embeddings {tilde over (H)}^(p)={{tildeover (h)}_(i) ^(p)}_(i=1) ^(N) to obtain contextualized passageembeddings H ^(p)∈

^(F×N).

On the other hand, for the answer text X^(a), simply concatenate itsGloVe embedding G^(a) and its BERT embedding B^(a) to obtain its wordembedding matrix H^(a)∈

^(d′×L). Another BiLSTM is then applied to the concatenated answerembedding sequence to obtain the contextualized answer embeddings H^(a)∈

^(F×L).

Hidden-Level Alignment: In the hidden-level alignment stage, performanother soft-alignment based on the contextualized passage and answerembeddings. Similarly, compute the aligned answer embedding, andconcatenate it with the contextualized passage embedding to obtain thefinal passage embedding matrix Align([G^(p); B^(p); {tilde over(H)}^(p)], [G^(a); B^(a); H ^(ā)], H ^(p) , H ^(a)) Finally, applyanother BiLSTM to the above concatenated embedding to obtain an F×Npassage embedding matrix X.

Bidirectional Graph-To-Sequence Generator: While RNNs are good atcapturing local dependencies among consecutive words in text, GNNs 309have been shown to better utilize the rich hidden text structureinformation such as syntactic parsing or semantic parsing, and can modelthe global interactions (relations) among sequence words to furtherimprove the representations. Therefore, unlike most of the existingmethods that rely on RNNs to encode the input passage, one or moreembodiments first construct a passage graph G from text where eachpassage word is treated as a graph node (e.g. 319 in graph 315), andthen employ a novel Graph2Seq model to encode the passage graph (andanswer), and to decode the natural language question.

Passage Graph Construction: Existing GNNs assume a graph structuredinput and directly consume it for computing the corresponding nodeembeddings. However, one or more embodiments construct a graph from thetext. Although there are early attempts on constructing a graph from asentence, there is no clear answer as to the best way of representingtext as a graph. One or more embodiments employ static and/or dynamicgraph construction approaches, exemplary performance differences betweenthese two methods are discussed elsewhere herein with regard toexperiments.

Syntax-based static graph construction: Construct a directed andunweighted passage graph based on dependency parsing. For each sentencein a passage, first obtain its dependency parse tree. Then, connectneighboring dependency parse trees by connecting those nodes that are ata sentence boundary and next to each other in text.

Semantics-aware dynamic graph construction: Dynamically build a directedand weighted graph to model semantic relationships among passage words.One or more embodiments make the process of building such a graph dependon not only the passage, but also on the answer. The graph constructionprocedure includes three steps: i) compute a dense adjacency matrix Afor the passage graph by applying self-attention to the word-levelpassage embeddings {tilde over (H)}_(p), ii) a kNN-style graphsparsification strategy is adopted to obtain a sparse adjacency matrixĀ, where only the K nearest neighbors (including itself) are kept aswell as the associated attention scores (i.e., the remaining attentionscores are masked off) for each node; and iii) based on BiLSTM overLSTM, also compute two normalized adjacency matrices A^(┤) and A^(├)according to their incoming and outgoing directions, by applying thesoftmax operation on the resulting sparse adjacency matrix Ā and itstranspose, respectively. Thus:

A=ReLU(U{tilde over (H)} ^(p))^(T)ReLU(U{tilde over (H)} ^(p)),Ā=kNN(A), A ^(┤) ,A ^(├)=softmax({Ā,Ā ^(T)})  (3)

where U is d×({tilde over (F)}_(p)+{tilde over (F)}_(a)) trainableweight matrix. Note that the supervision signal is able toback-propagate through the kNN-style graph sparsification operationsince the K nearest attention scores are kept.

Bidirectional Gated Graph Neural Networks: To effectively learn thegraph embeddings from the constructed text graph, one or moreembodiments employ a novel Bidirectional Gated Graph Neural Network(BiGGNN) which extends Gated Graph Sequence Neural Networks by learningnode embeddings from both incoming 321, 323 and outgoing 325, 327 edgesin an interleaved fashion when processing the directed passage graph. Asimilar idea has also been exploited by extending another popularvariant of GNNs-GraphSAGE. However, one pertinent distinction betweenBiGGNN as disclosed herein and the prior-art bidirectional GraphSAGE isthat one or more embodiments fuse the intermediate node embeddings fromboth incoming and outgoing edges in every iteration during the training,whereas the prior art model simply trains the node embeddings of eachdirection independently and concatenates them in the final step.

In BiGGNN, node embeddings are initialized to the passage embeddings Xreturned by DAN. The same set of network parameters are shared at everyhop of computation. At each computation hop, for every node in thegraph, apply an aggregation function which takes as input a set ofincoming (or outgoing) neighboring node vectors and outputs a backward(or forward) aggregation vector. For the syntax-based static graph, usea mean aggregator for simplicity although other operators such as max orattention could also be employed:

=MEAN({h _(v) ^(k-1) }∪{h _(u) ^(k-1) ,∀u∈

_(┤(v))})

=MEAN({h _(v) ^(k-1) }∪{h _(u) ^(k-1) ,∀u∈

_(├(v))})  (4)

For the semantics-aware dynamic graph, compute a weighted average foraggregation where the weights come from the normalized adjacencymatrices A^(┤) and A^(├), defined as:

$\begin{matrix}{{h_{_{\dashv {(v)}}}^{K} = {\sum\limits_{\forall{u \in _{\dashv {(v)}}}}{a_{\upsilon,u}^{\dashv}h_{u}^{k - 1}}}},{h_{_{\vdash {(v)}}}^{K} = {\sum\limits_{\forall{u \in _{\vdash {(v)}}}}{a_{\upsilon,u}^{\vdash}h_{u}^{k - 1}}}}} & (5)\end{matrix}$

While some prior art techniques learn separate node embeddings for bothdirections independently, one or more embodiments fuse the informationaggregated in the two directions at each hop, which we have found worksbetter in general (see 317):

=Fuse(

,

)  (6)

One or more embodiments employ a fusion function as a gated sum of twoinformation sources:

Fuse(a,b)=z⊙a+(1−z)⊙b, z=σ(W _(z)[a;b;a⊙b;a−b]+b _(z))  (7)

In the above, ⊙ is the component-wise multiplication, σ is a sigmoidfunction, and z is a gating vector.

Finally, a Gated Recurrent Unit (GRU) is used to update the nodeembeddings by

incorporating the aggregation information:

h _(v) ^(k)=GRU(h _(v) ^(k-1),

)  (8)

After n hops of GNN computation, where n is a hyperparameter, obtain thefinal state embedding h_(v) ^(n) for node v. As seen at 337, to computethe graph-level embedding, first apply a linear projection to the nodeembeddings, and then apply max-pooling over all node embeddings to get,at 333, a d-dim vector h^(G).

RNN Decoder: On the decoder side, one or more embodiments adopt the samemodel architecture as other state-of-the-art Seq2Seq models where anattention-based LSTM decoder 335 with copy and coverage mechanisms isemployed. The decoder takes the graph-level embedding h^(G) followed bytwo separate fully-connected layers as initial hidden states (i.e., c₀and s₀) and the node embeddings {h_(v) ^(n),∀v∈

} as the attention memory, and generates the output sequence one word ata time. Further details are provided elsewhere herein.

Hybrid Evaluator: Regarding 341, it has been observed that optimizingsuch cross-entropy based training objectives for sequence learning doesnot always produce the best results on discrete evaluation metrics.Limitations of this strategy include exposure bias and evaluationdiscrepancy between training and testing. To tackle these issues, somerecent QG approaches directly optimize evaluation metrics usingREINFORCE. One or more embodiments further employ a mixed objectivefunction with both syntactic and semantic constraints for guiding textgeneration. In particular, one or more embodiments provide a hybridevaluator with a mixed objective function that combines bothcross-entropy loss 343 and RL loss 345 in order to ensure the generationof syntactically and semantically valid text.

For the RL part 345, one or more embodiments employ the self-criticalsequence training (SCST) algorithm to directly optimize the evaluationmetrics. SCST is an efficient REINFORCE algorithm that utilizes theoutput of its own test-time inference algorithm to normalize the rewardsit experiences. In SCST, at each training iteration, the model generatestwo output sequences: the sampled output Y^(s), produced by multinomialsampling, that is, each word y_(t) ^(s) is sampled according to thelikelihood P(y_(t)|X,y<t) predicted by the generator, and the baselineoutput Ŷ, obtained by greedy search, that is, by maximizing the outputprobability distribution at each decoding step. Define r(Y) as thereward of an output sequence Y, computed by comparing it tocorresponding ground-truth sequence Y* with some reward metrics. Theloss function is defined as:

$\begin{matrix}{\mathcal{L}_{rl} = {\left( {{r\left( \hat{Y} \right)} - {r\left( Y^{s} \right)}} \right){\sum\limits_{t}{{\log P}\left( {\left. y_{t}^{s} \middle| X \right.,y_{< t}^{s}} \right)}}}} & (9)\end{matrix}$

As can be seen, if the sampled output has a higher reward than thebaseline one, maximize its likelihood, and vice versa.

One pertinent factor for RL is to pick the proper reward function. Totake syntactic and semantic constraints into account, consider thefollowing metrics as reward functions:

Evaluation metric as reward function: use one of the evaluation metrics,BLEU-4, as reward function ƒ_(eval), which permits directly optimizingthe model towards the evaluation metrics.

Semantic metric as reward function: One drawback of some evaluationmetrics like BLEU is that they do not measure meaning, but only rewardsystems for n-grams that have exact matches in the reference system. Tomake the reward function more effective and robust, one or moreembodiments additionally use word movers distance (WMD) as a semanticreward function ƒ_(sem). WMD is a state-of-the-art approach to measurethe dissimilarity between two sentences based on word. One or moreembodiments take the negative of the WMD distance between a generatedsequence and the ground-truth sequence and divide it by the sequencelength as its semantic score.

The final reward function 347 is defined as:

r(Y)=ƒ_(eval)(Y,Y*)+αƒ_(sem)(Y,Y*)

where α is a scalar.

Training and Testing: one or more embodiments train the model in twostages. In the first state, train the model using regular cross-entropyloss, defined as:

$\begin{matrix}{\mathcal{L}_{lm} = {{\sum\limits_{t}{- {{\log P}\left( {\left. y_{t}^{*} \middle| X \right.,y_{< t}^{*}} \right)}}} + {\lambda {covloss}}_{t}}} & (10)\end{matrix}$

In the above, y_(t)* is the word at the t-th position of theground-truth output sequence and covloss_(t) is the coverage lossdefined as Σ_(i) min(a_(i) ^(t),c_(i) ^(t)), with a_(i) ^(t) being thei-th element of the attention vector over the input sequence at timestep t. Scheduled teacher forcing is adopted to alleviate the exposurebias problem. In the second stage, fine-tune the model by optimizing amixed objective function combining both cross-entropy loss and RL loss,defined as:

=γ

rl+(1−γ)

lm  (11)

In the above, γ is a scaling factor controlling the trade-off betweencross-entropy loss and RL loss. During the testing phase, use beamsearch to generate final predictions.

Experimental results: An exemplary inventive model was evaluated againststate-of-the-art methods on the SQuAD dataset. Exemplary full modelshave two variants, namely, G2S_(sta)+BERT+RL and G2S_(dyn)+BERT+RL whichadopt static graph construction or dynamic graph construction,respectively. Exemplary model settings and sensitivity analysis arepresented elsewhere herein.

Baseline methods: Comparison was conducted against the followingbaselines: i) SeqCopyNet, ii) NQG++, iii) MPQG+R, iv) AFPQA, v)s2sa-at-mp-gsa, vi) ASs2s, and vii) CGC-QG.

Data and Metrics: SQuAD contains more than 100K questions posed by crowdworkers on 536 Wikipedia articles. Since the test set of the originalSQuAD is not publicly available, the accessible parts were used as theentire dataset in the experiments. For fair comparison with previousmethods, an exemplary inventive model was evaluated on both data split-1that contains 75,500/17,934/11,805 (train/development/test) examples anddata split-2 that contains 86,635/8,965/8,964 examples.

BLEU-4, METEOR, ROUGE-L and Q-BLEU1 were employed as evaluation metrics.Initially, BLEU-4 and METEOR were designed for evaluating machinetranslation systems and ROUGE-L was designed for evaluating textsummarization systems. Recently, Q-BLEU1 was designed for betterevaluating question generation systems, which was shown to correlatesignificantly better with human judgments compared to existing metrics.

Besides automatic evaluation metrics, a human evaluation study was alsoconducted on split-2. Human evaluators were asked to rate generatedquestions from a set of anonymized competing systems based on whetherthey were syntactically correct, semantically correct, and relevant tothe passage. The rating scale is from 1 to 5, on each of the threecategories. Evaluation scores from all evaluators were collected andaveraged as final scores. Further details are provided elsewhere herein.

The table of FIG. 5 shows the automatic evaluation results comparingexemplary models against other state-of-the-art baseline methods(automatic evaluation results on the SQuAD test set). It can be seenthat both exemplary full models G2Ssta+BERT+RL and G2Sdyn+BERT+RL workwell on both data splits and consistently outperform previous methods bya significant margin. This demonstrates that the RL-based Graph2Seqmodel, together with the deep alignment network, successfully addressesthe three issues with prior-art techniques mentioned above. Betweenthese two variants, G2Ssta+BERT+RL outperforms G2Sdyn+BERT+RL on all themetrics. Also, unlike the baseline methods, the exemplary model does notrely on any hand-crafted rules or ad-hoc strategies, and is fullyend-to-end trainable.

The table of FIG. 6 shows human evaluation results (+/−standarddeviation) on the SQuAD split-2 test set. The rating scale is from 1 to5 (higher scores indicate better results). FIG. 6 presents a humanevaluation study to assess the quality of the questions generated by anexemplary model, the baseline method MPQG+R, and the ground-truth datain terms of syntax, semantics and relevance metrics. It can be seen thatthe best performing model achieves good results even compared to theground-truth, and outperforms the strong baseline method MPQG+R. Erroranalysis shows that main syntactic error occurs in repeated/unknownwords in generated questions. Further, the slightly lower quality onsemantics also impacts the relevance.

The table of FIG. 7 shows an ablation study on the SQuAD split-2 testset. As shown, an ablation study was performed to systematically assessthe impact of different model components (e.g., BERT, RL, DAN, andBiGGNN) for two proposed full model variants (static vs dynamic) on theSQuAD split-2 test set. It confirms that syntax-based static graphconstruction (G2Ssta+BERT+RL) performs better than semantics-awaredynamic graph construction (G2Sdyn+BERT+RL) in almost every setting. Anadvantage of static graph construction is that useful domain knowledgecan be hard-coded into the graph, which can greatly benefit thedownstream task. However, it might suffer if there is a lack of priorknowledge for a specific domain knowledge. On the other hand, dynamicgraph construction does not need any prior knowledge about the hiddenstructure of text, and only relies on the attention matrix to capturethis structured information, which provides an easy way to achieve adecent performance.

By turning off the Deep Alignment Network (DAN), the BLEU-4 score ofG2S_(sta) (similarly for G2S_(dyn)) dramatically drops from 16.96% to12.62%, which indicates the importance of answer information for QG andshows the effectiveness of DAN. This can also be verified by comparingthe performance between the DAN-enhanced Seq2Seq model (16.14 BLEU-4score) and other carefully designed answer-aware Seq2Seq baselines suchas NQG++(13.29 BLEU-4 score), MPQG+R (14.71 BLEU-4 score) and AFPQA(15.82 BLEU-4 score). Further experiments demonstrate that bothword-level (G2S_(sta) w/DAN-word only) and hidden-level (G2S_(sta)w/DAN-hidden only) answer alignments in DAN are helpful.

The advantages of Graph2Seq learning over Seq2Seq learning on this taskby can be seen by comparing the performance between G2S_(sta) andSeq2Seq. Compared to Seq2Seq based QG methods that completely ignorehidden structure information in the passage, an exemplary Graph2Seqbased method is aware of more hidden structure information such assemantic similarity between any pair of words that are not directlyconnected or syntactic relationships between two words captured in adependency parsing tree. In experiments, it was observed that that doingboth forward and backward message passing in the GNN encoder isbeneficial. It appears that using GCN as the graph encoder (andconverting the input graph to an undirected graph) does not necessarilyprovide good performance. In addition, fine-tuning the model usingREINFORCE can further improve the model performance in all settings(i.e., with and without BERT), which shows the benefits of directlyoptimizing the evaluation metrics. Besides, it was found that thepretrained BERT embedding has a considerable impact on the performanceand fine-tuning BERT embedding even further improves the performance,which demonstrates the power of large-scale pretrained language models.

The table of FIG. 8 depicts generated questions on the SQuAD split-2test set. Target answers are underlined. This table further show a fewexamples that illustrate the quality of generated text given a passageunder different ablated systems. As can be seen, incorporating answerinformation helps the model identify the answer type of the question tobe generated, and thus makes the generated questions more relevant andspecific. Also, it was noted that an embodiment of the inventiveGraph2Seq model can generate more complete and valid questions comparedto the Seq2Seq baseline. It is believed that the Graph2Seq model is ableto exploit the rich text structure information better than a Seq2Seqmodel. Lastly, note that fine-tuning the model using REINFORCE canimprove the quality of the generated questions.

One or more embodiments thus provide a novel RL based Graph2Seq modelfor QG, where the answer information is utilized by an effective DeepAlignment Network and a novel bidirectional GNN is provided to processthe directed passage graph. A two-stage training strategy benefits fromboth cross-entropy based and REINFORCE based sequence training. Staticand/or dynamic graph construction from text are possible. On thebenchmark SQuAD dataset, an exemplary embodiments outperforms previousstate-of-the-art methods by a significant margin and achieves new bestresults.

Additional Details on the RNN Decoder: In one or more embodiments, ateach decoding step t, an attention mechanism learns to attend to themost relevant words in the input sequence, and computes a context vectorh_(t)* based on the current decoding state St, the current coveragevector c^(t) and the attention memory. In addition, the generationprobability p_(gen)∈[0, 1] is calculated from the context vector h_(t)*,the decoder state st and the decoder input y_(t-1). Next, p_(gen) isused as a soft switch to choose between generating a word from thevocabulary, or copying a word from the input sequence. One or moreembodiments dynamically maintain an extended vocabulary which is theunion of the usual vocabulary and all words appearing in a batch ofsource examples (i.e., passages and answers). Finally, in order toencourage the decoder to utilize the diverse components of the inputsequence, a coverage mechanism is applied. At each step, maintain acoverage vector c^(t), which is the sum of attention distributions overall previous decoder time steps. A coverage loss is also computed topenalize repeatedly attending to the same locations of the inputsequence.

Additional details on model settings: One or more embodiments keep andfix the 300-dim GloVe vectors for the most frequent 70,000 words in thetraining set. Compute the 1024-dim BERT embeddings on the fly for eachword in text using a (trainable) weighted sum of all BERT layer outputs.The embedding sizes of case, POS and NER tags are set to 3, 12 and 8,respectively. Set the hidden state size of BiLSTM to 150 so that theconcatenated state size for both directions is 300. The size of allother hidden layers is set to 300. Apply a variational dropout rate of0.4 after word embedding layers and 0.3 after RNN layers. Set theneighborhood size to 10 for dynamic graph construction. The number ofGNN hops is set to 3. During training, in each epoch, set the initialteacher forcing probability to 0.75 and exponentially increase it to0.75*0.9999′ where i is the training step. Set α in the reward functionto 0.1, γ in the mixed loss function to 0.99, and the coverage lossratio λ to 0.4. Use Adam (known to the skilled artisan) as theoptimizer, and the learning rate is set to 0.001 in the pretrainingstage and 0.00001 in the fine-tuning stage. Reduce the learning rate bya factor of 0.5 if the validation BLEU-4 score stops improving for threeepochs. Stop the training when no improvement is seen for 10 epochs.Clip the gradient at length 10. The batch size is set to 60 and 50 ondata split-1 and split-2, respectively. The beam search width is set to5. All hyperparameters are tuned on the development set.

Sensitivity Analysis of Hyperparameters: To study the effect of thenumber of GNN hops, experiments were conducted on the G2S_(sta) model onthe SQuAD split-2 data. FIG. 9 shows that an exemplary model is not verysensitive to the number of GNN hops and can achieve reasonably goodresults with various number(s) of hops.

Details on Human Evaluation: a small-scale (i.e., 50 random examples persystem) human evaluation was conducted on the split-2 data. Five humanevaluators were asked to give feedback on the quality of questionsgenerated by a set of anonymized competing systems. In each example,given a triple containing a source passage, a target answer and ananonymized system output, the evaluators were asked to rate the qualityof the output by answering the following three questions: i) is thisgenerated question syntactically correct? ii) is this generated questionsemantically correct? and iii) is this generated question relevant tothe passage? For each evaluation question, the rating scale is from 1 to5 where a higher score means better quality (i.e., 1: Poor, 2: Marginal,3: Acceptable, 4: Good, 5: Excellent). Responses from all evaluatorswere collected and averaged.

Additional Details on Ablation Study: A comprehensive ablation study wasperformed to systematically assess the impact of different modelcomponents (e.g., BERT, RL, DAN, BiGGNN, FEAT, DAN-word, and DAN-hidden)for two proposed full model variants (static vs dynamic) on the SQuADsplit-2 test set. Experimental results, shown in FIG. 10, confirmed thatthe components in one or more embodiments each make a contribution tothe overall performance.

One or more embodiments provide techniques for using a computing deviceto generate a natural language question for utilization with a dialogsystem, including receiving, by a computing device, an input passage toask a natural language question about. The natural language question isgenerated by the computing device. Also included are receiving, by thecomputing device, a target answer; and constructing, by the computingdevice, an input passage graph. The input passage graph includes one ormore nodes, where each node represents a word in the input passage.Aspects further include utilizing, by the computing device, the inputpassage graph to generate the natural language question to result in thetarget answer.

In some instances, a bidirectional gated neural network is utilized toencode the input passage graphs.

One or more embodiments generate questions based on both the given textand answer information, which later can be used to train other systemssuch as a dialog agent. One or more embodiments employ advanced AItechniques such as graph neural networks with reinforcement learningfrom any given text and answer information.

Given the discussion thus far, it will be appreciated that, in generalterms, an exemplary method, according to an aspect of the invention,includes for a passage text 305 and a corresponding answer text 307,performing a word-level soft alignment to obtain contextualized passageembeddings and contextualized answer embeddings; e.g., using network303. Further steps include performing a hidden level soft alignment onthe contextualized passage embeddings and the contextualized answerembeddings to obtain a passage embedding matrix; e.g., using network303; and constructing a passage graph of the passage text based on thepassage embedding matrix; e.g., using graph encoder 313. Still furthersteps include applying a bidirectional gated graph neural network 309 tothe passage graph until a final state embedding is determined, duringwhich application intermediate node embeddings are fused from bothincoming 331 and outgoing 329 edges of the graph; obtaining agraph-level embedding 333 from the final state embedding; and decodingthe final state embedding to generate an output sequence word-by-word(e.g., with decoder 335). An even further step includes training amachine learning model to generate at least one question correspondingto the passage text and the answer text, by evaluating the outputsequence with a hybrid evaluator 341 combining cross-entropy evaluation343 and reinforcement learning evaluation 345.

In some instances, constructing the passage graph of the passage textbased on the passage embedding matrix includes initializing nodeembeddings for the passage graph to correspond to the passage embeddingmatrix (refer to above discussion wherein, in BiGGNN, node embeddingsare initialized to the passage embeddings X returned by DAN. Further, insome instances, applying the bidirectional gated graph neural network tothe passage graph until the final state embedding is determinedincludes, starting with the initial node embeddings, iterativelydetermining a plurality of the intermediate node embeddings with thebidirectional gated graph neural network, until the final stateembedding is determined, the intermediate node embeddings are fused fromboth the incoming and the outgoing edges of the graph during eachiteration. Refer to above discussion wherein, in one or moreembodiments, the intermediate node embeddings are fused from bothincoming and outgoing edges in every iteration during the training. Inone or more cases, obtain the graph-level embedding from the final stateembedding by applying linear projection and max pooling 337 to the finalstate embedding to obtain the graph-level embedding.

One or more embodiments further include using the trained machinelearning module to respond to a user query. For example, the passagetext, the answer text, the at least one question, and the user query canpertain to information technology, and a further step can includeconfiguring at least one information technology asset (see examples inFIGS. 1, 2, and 11) in accordance with the response.

In some cases, training the machine learning model by evaluating theoutput sequence with the hybrid evaluator includes optimizing a rewardfunction combining an evaluation metric reward function and a semanticreward function. Refer to the discussion of the hybrid evaluator 341.

In some cases, the training includes initial training with cross-entropyloss and fine-tuning to optimize a scaling factor combiningcross-entropy loss and reinforcement learning loss. Refer to Equations9, 10, and 11 and accompanying text: Eq. 9 is a reinforcement learningloss, and Eq. 10 is a regular loss—cross entropy. Eq. 11 is thecombination of them so that a better loss function can be obtained.

Note that Equations (1) and (2) and accompanying text discuss aspects ofsoft alignment which, as discussed, can be applied specifically toword-level alignment and hidden-level alignment.

In some instances, the passage text includes a first collection of wordtokens X^(p); the corresponding answer text includes a second collectionof word tokens X^(a); and, in the step of decoding the final stateembedding to generate the output sequence word-by-word, the outputsequence includes a sequence of word tokens Y which maximizes aconditional probability of a corresponding question sequence. Seediscussion above of Ŷ=arg max_(Y) P(Y|X^(p),X^(a)).

In another aspect, an exemplary apparatus includes a memory (e.g. 30); anon-transitory computer readable medium (e.g. 34) including computerexecutable instructions; and at least one processor 16, coupled to thememory and the non-transitory computer readable medium, and operative toexecute the instructions to be operative to instantiate a deep alignmentnetwork 303, a graph encoder 313 including a bidirectional gated graphneural network 309, a decoder 335, and a hybrid evaluator 341. Theelements are in data communication with each other; for example, theymay share data in common data structures in the memory.

One or more embodiments of the invention, or elements thereof, can beimplemented in the form of an apparatus including a memory and at leastone processor that is coupled to the memory and operative to performexemplary method steps. FIG. 11 depicts a computer system that may beuseful in implementing one or more aspects and/or elements of theinvention, also representative of a cloud computing node according to anembodiment of the present invention. Referring now to FIG. 11, cloudcomputing node 10 is only one example of a suitable cloud computing nodeand is not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 11, computer system/server 12 in cloud computing node10 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, and external disk drivearrays, RAID systems, tape drives, and data archival storage systems,etc.

Thus, one or more embodiments can make use of software running on ageneral purpose computer or workstation. With reference to FIG. 11, suchan implementation might employ, for example, a processor 16, a memory28, and an input/output interface 22 to a display 24 and externaldevice(s) 14 such as a keyboard, a pointing device, or the like. Theterm “processor” as used herein is intended to include any processingdevice, such as, for example, one that includes a CPU (centralprocessing unit) and/or other forms of processing circuitry. Further,the term “processor” may refer to more than one individual processor.The term “memory” is intended to include memory associated with aprocessor or CPU, such as, for example, RAM (random access memory) 30,ROM (read only memory), a fixed memory device (for example, hard drive34), a removable memory device (for example, diskette), a flash memoryand the like. In addition, the phrase “input/output interface” as usedherein, is intended to contemplate an interface to, for example, one ormore mechanisms for inputting data to the processing unit (for example,mouse), and one or more mechanisms for providing results associated withthe processing unit (for example, printer). The processor 16, memory 28,and input/output interface 22 can be interconnected, for example, viabus 18 as part of a data processing unit 12. Suitable interconnections,for example via bus 18, can also be provided to a network interface 20,such as a network card, which can be provided to interface with acomputer network, and to a media interface, such as a diskette or CD-ROMdrive, which can be provided to interface with suitable media.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and implemented by a CPU.Such software could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 16 coupled directly orindirectly to memory elements 28 through a system bus 18. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories 32 which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, and the like) can be coupled to the systemeither directly or through intervening I/O controllers.

Network adapters 20 may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 12 as shown in FIG. 11)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

One or more embodiments can be at least partially implemented in thecontext of a cloud or virtual machine environment, although this isexemplary and non-limiting. Reference is made back to FIGS. 1-2 andaccompanying text. Consider, e.g., a cloud-based service 96 forfine-grained visual recognition in mobile augmented reality, located inlayer 90.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the appropriate elements depicted inthe block diagrams and/or described herein; by way of example and notlimitation, any one, some or all of the modules/blocks and orsub-modules/sub-blocks described. The method steps can then be carriedout using the distinct software modules and/or sub-modules of thesystem, as described above, executing on one or more hardware processorssuch as 16. Further, a computer program product can include acomputer-readable storage medium with code adapted to be implemented tocarry out one or more method steps described herein, including theprovision of the system with the distinct software modules.

One example of user interface that could be employed in some cases ishypertext markup language (HTML) code served out by a server or thelike, to a browser of a computing device of a user. The HTML is parsedby the browser on the user's computing device to create a graphical userinterface (GUI).

Exemplary System and Article of Manufacture Details

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method comprising: for a passage text and acorresponding answer text, performing a word-level soft alignment toobtain contextualized passage embeddings and contextualized answerembeddings; performing a hidden level soft alignment on saidcontextualized passage embeddings and said contextualized answerembeddings to obtain a passage embedding matrix; constructing a passagegraph of said passage text based on said passage embedding matrix;applying a bidirectional gated graph neural network to said passagegraph until a final state embedding is determined, during whichapplication intermediate node embeddings are fused from both incomingand outgoing edges of said graph; obtaining a graph-level embedding fromsaid final state embedding; decoding said final state embedding togenerate an output sequence word-by-word; and training a machinelearning model to generate at least one question corresponding to saidpassage text and said answer text, by evaluating said output sequencewith a hybrid evaluator combining cross-entropy evaluation andreinforcement learning evaluation.
 2. The method of claim 1, wherein:constructing said passage graph of said passage text based on saidpassage embedding matrix includes initializing node embeddings for saidpassage graph to correspond to said passage embedding matrix; applyingsaid bidirectional gated graph neural network to said passage graphuntil said final state embedding is determined comprises, starting withsaid initial node embeddings, iteratively determining a plurality ofsaid intermediate node embeddings with said bidirectional gated graphneural network, until said final state embedding is determined, whereinsaid intermediate node embeddings are fused from both said incoming andsaid outgoing edges of said graph during each iteration; and obtainingsaid graph-level embedding from said final state embedding comprisesapplying linear projection and max pooling to said final state embeddingto obtain said graph-level embedding.
 3. The method of claim 2, furthercomprising using said trained machine learning module to respond to auser query.
 4. The method of claim 3, wherein said passage text, saidanswer text, said at least one question, and said user query pertain toinformation technology, further comprising configuring at least oneinformation technology asset in accordance with said response.
 5. Themethod of claim 2, wherein training said machine learning model byevaluating said output sequence with said hybrid evaluator comprisesoptimizing a reward function combining an evaluation metric rewardfunction and a semantic reward function.
 6. The method of claim 2,wherein said training comprises initial training with cross-entropy lossand fine-tuning to optimize a scaling factor combining cross-entropyloss and reinforcement learning loss.
 7. The method of claim 2, wherein:said passage text comprises a first collection of word tokens; saidcorresponding answer text comprises a second collection of word tokens;and in said step of decoding said final state embedding to generate saidoutput sequence word-by-word, said output sequence comprises a sequenceof word tokens which maximizes a conditional probability of acorresponding question sequence.
 8. A non-transitory computer readablemedium comprising computer executable instructions which when executedby a computer cause the computer to perform a method of: for a passagetext and a corresponding answer text, performing a word-level softalignment to obtain contextualized passage embeddings and contextualizedanswer embeddings; performing a hidden level soft alignment on saidcontextualized passage embeddings and said contextualized answerembeddings to obtain a passage embedding matrix; constructing a passagegraph of said passage text based on said passage embedding matrix;applying a bidirectional gated graph neural network to said passagegraph until a final state embedding is determined, during whichapplication intermediate node embeddings are fused from both incomingand outgoing edges of said graph; obtaining a graph-level embedding fromsaid final state embedding; decoding said final state embedding togenerate an output sequence word-by-word; and training a machinelearning model to generate at least one question corresponding to saidpassage text and said answer text, by evaluating said output sequencewith a hybrid evaluator combining cross-entropy evaluation andreinforcement learning evaluation.
 9. The non-transitory computerreadable medium of claim 8, wherein: constructing said passage graph ofsaid passage text based on said passage embedding matrix includesinitializing node embeddings for said passage graph to correspond tosaid passage embedding matrix; applying said bidirectional gated graphneural network to said passage graph until said final state embedding isdetermined comprises, starting with said initial node embeddings,iteratively determining a plurality of said intermediate node embeddingswith said bidirectional gated graph neural network, until said finalstate embedding is determined, wherein said intermediate node embeddingsare fused from both said incoming and said outgoing edges of said graphduring each iteration; and obtaining said graph-level embedding fromsaid final state embedding comprises applying linear projection and maxpooling to said final state embedding to obtain said graph-levelembedding.
 10. The non-transitory computer readable medium of claim 9,wherein said method further comprises using said trained machinelearning module to respond to a user query.
 11. The non-transitorycomputer readable medium of claim 9, wherein training said machinelearning model by evaluating said output sequence with said hybridevaluator comprises optimizing a reward function combining an evaluationmetric reward function and a semantic reward function.
 12. Thenon-transitory computer readable medium of claim 9, wherein saidtraining comprises initial training with cross-entropy loss andfine-tuning to optimize a scaling factor combining cross-entropy lossand reinforcement learning loss.
 13. The non-transitory computerreadable medium of claim 9, wherein: said passage text comprises a firstcollection of word tokens; said corresponding answer text comprises asecond collection of word tokens; and in said method step of decodingsaid final state embedding to generate said output sequenceword-by-word, said output sequence comprises a sequence of word tokenswhich maximizes a conditional probability of a corresponding questionsequence.
 14. An apparatus comprising: a memory; a non-transitorycomputer readable medium comprising computer executable instructions;and at least one processor, coupled to said memory and saidnon-transitory computer readable medium, and operative to execute saidinstructions to be operative to: instantiate a deep alignment network, agraph encoder including a bidirectional gated graph neural network, adecoder, and a hybrid evaluator; with said deep alignment network, for apassage text and a corresponding answer text, perform a word-level softalignment to obtain contextualized passage embeddings and contextualizedanswer embeddings; with said deep alignment network, perform a hiddenlevel soft alignment on said contextualized passage embeddings and saidcontextualized answer embeddings to obtain a passage embedding matrix;with said graph encoder, construct a passage graph of said passage textbased on said passage embedding matrix; apply said bidirectional gatedgraph neural network to said passage graph until a final state embeddingis determined, during which application intermediate node embeddings arefused from both incoming and outgoing edges of said graph; with saidgraph encoder, obtain a graph-level embedding from said final stateembedding; with said decoder, decode said final state embedding togenerate an output sequence word-by-word; and train a machine learningmodel to generate at least one question corresponding to said passagetext and said answer text, by evaluating said output sequence with saidhybrid evaluator combining cross-entropy evaluation and reinforcementlearning evaluation.
 15. The apparatus of claim 14, wherein:constructing said passage graph of said passage text based on saidpassage embedding matrix includes initializing node embeddings for saidpassage graph to correspond to said passage embedding matrix; applyingsaid bidirectional gated graph neural network to said passage graphuntil said final state embedding is determined comprises, starting withsaid initial node embeddings, iteratively determining a plurality ofsaid intermediate node embeddings with said bidirectional gated graphneural network, until said final state embedding is determined, whereinsaid intermediate node embeddings are fused from both said incoming andsaid outgoing edges of said graph during each iteration; and obtainingsaid graph-level embedding from said final state embedding comprisesapplying linear projection and max pooling to said final state embeddingto obtain said graph-level embedding.
 16. The apparatus of claim 15,wherein said at least one processor is further operative to execute saidinstructions to use said trained machine learning module to respond to auser query.
 17. The apparatus of claim 16, wherein said passage text,said answer text, said at least one question, and said user querypertain to information technology, wherein said at least one processoris further operative to execute said instructions to configure at leastone information technology asset in accordance with said response. 18.The apparatus of claim 15, wherein training said machine learning modelby evaluating said output sequence with said hybrid evaluator comprisesoptimizing a reward function combining an evaluation metric rewardfunction and a semantic reward function.
 19. The apparatus of claim 15,wherein said training comprises initial training with cross-entropy lossand fine-tuning to optimize a scaling factor combining cross-entropyloss and reinforcement learning loss.
 20. The apparatus of claim 15,wherein: said passage text comprises a first collection of word tokens;said corresponding answer text comprises a second collection of wordtokens; and in said method step of decoding said final state embeddingto generate said output sequence word-by-word, said output sequencecomprises a sequence of word tokens which maximizes a conditionalprobability of a corresponding question sequence.